Combining Variation Simulation With Welding Simulation for Prediction of Deformation and Variation of a Final Assembly
Bibliographic record
Abstract
In most variation simulations, i.e., simulations of geometric variations in assemblies, the influence from heating and cooling processes, generated when two parts are welded together, is not taken into consideration. In most welding simulations, the influence from geometric tolerances on parts is not taken into consideration, i.e., the simulations are based on nominal parts. In this paper, these two aspects, both crucial for predicting the final outcome of an assembly, are combined. Monte Carlo simulation is used to generate a number of different non-nominal parts in a software for variation simulation. The translation and rotation matrices, representing the deviations from the nominal geometry due to positioning error, are exported to a software for welding simulation, where the effects from welding are applied. The final results are then analyzed with respect to both deviation and variation. The method is applied on a simple case, a T-weld joint, with available measurements of residual stresses and deformations. The effect of the different sources of deviation on the final outcome is analyzed and the difference between welding simulations applied to nominal parts and to disturbed (non-nominal) parts is investigated. The study shows that, in order to achieve realistic results, variation simulations should be combined with welding simulations. It does also show that welding simulations should be applied to a set of non-nominal parts since the difference between deviation of a nominal part and deviation of a non-nominal part due to influence of welding can be quite large.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".